Object Classification by means of Multi-Feature Concept Learning in a Multi Expert-Agent System

TL;DR

This paper presents a multi-agent system for object classification that leverages cooperative expert agents and online feature learning, resulting in improved performance and reduced message passing overload.

Contribution

It introduces a multi-agent classification framework with cooperative expert agents and online feature learning, enhancing accuracy and efficiency over prior methods.

Findings

Better classification performance compared to previous approaches.

Reduced message passing overload in the multi-agent system.

Enhanced system operability through expert cooperation.

Abstract

Classification of some objects in classes of concepts is an essential and even breathtaking task in many applications. A solution is discussed here based on Multi-Agent systems. A kernel of some expert agents in several classes is to consult a central agent decide among the classification problem of a certain object. This kernel is moderated with the center agent, trying to manage the querying agents for any decision problem by means of a data-header like feature set. Agents have cooperation among concepts related to the classes of this classification decision-making; and may affect on each others' results on a certain query object in a multi-agent learning approach. This leads to an online feature learning via the consulting trend. The performance is discussed to be much better in comparison to some other prior trends while system's message passing overload is decreased to less agents and the expertism helps the performance and operability of system win the comparison.